A Case Study of a Variance Analysis Framework for Managing Distribution Costs*

Abstract

Managing the distribution function as part of an overall supply‐chain management strategy has become increasingly important given rising fuel costs in recent years. This paper presents a comprehensive variance analysis framework developed by supply‐chain managers at Catalyst Paper Corporation as a tool for reporting and controlling distribution costs. The model decomposes the overall static‐budget variance into four primary variance categories: volume, customer mix, distribution mix, and carrier charges. The framework addresses key limitations in the coverage of variance analysis contained in many management accounting textbooks. Specifically, Catalyst's framework incorporates: (a) mix variance calculations where there is more than one mix factor within a single cost element; (b) the impact of unplanned and unrealized activities; and (c) multiple nested mix variance calculations. Although developed in the context of distribution costs, the framework can be applied to the analysis of other manufacturing and non‐manufacturing costs where multiple mix factors exist. L'importance de la gestion de la fonction de distribution dans le cadre de la stratégie globale de gestion de la chaîne d'approvisionnement s'est accrue avec la hausse des coûts du carburant des dernières années. Les auteurs présentent un cadre complet d'analyse des écarts, élaboré par les gestionnaires de la chaîne d'approvisionnement chez Catalyst Paper Corporation aux fins de la présentation et du contrôle des coûts de distribution. Le modèle décompose l'écart global du budget fixe en quatre grandes catégories d'écarts: les écarts sur volume, les écarts sur composition de la clientèle, les écarts sur composition de la distribution et les écarts sur frais de transport. Le cadre résout les principales limites de la couverture de l'analyse des écarts évoquées dans de nombreux manuels de comptabilité de management. Le cadre d'analyse de Catalyst Paper Corporation englobe: a) les calculs de l'écart sur composition lorsqu'il existe plus d'un facteur de composition dans un même élément de coût; b) l'incidence des activités non planifiées et non réalisées; et c) les calculs de l'écart sur composition à multiples critères de classification. Bien qu'il ait été élaboré dans le contexte des coûts de distribution, ce cadre peut être appliqué à l'analyse d'autres coûts liés ou non à la fabrication, lorsque les facteurs de composition sont multiples.

Using Prototypes to Induce Experimentation and Knowledge Integration in the Development of Enabling Accounting Information*

“…A second (albeit smaller) contribution of this study is to document an actual PMS for managing transportation costs, based on variance analysis. This has received little attention in the literature, with Gaffney, Gladkikh, and Webb 2007 as a notable exception. To the extent that variance analysis has been addressed in the literature, the emphasis has been on manufacturing settings.…”

“…For example, Emsley (2001) investigates how variance analysis can improve problem solving, using a longitudinal case study in a manufacturing company; Balakrishnan and Sprinkle (2002) discuss an approach for teaching variance analysis in connection with capacity costing, using a manufacturing setting; textbooks by Horngren, Bhimani, Datar, and Foster 2005 and Drury 2004 provide teaching examples in manufacturing. The application of variance analysis to other kinds of activities is relatively unexplored (Gaffney et al 2007). However, there are conceptual differences between manufacturing and transportation, such as the basis for establishing the “standard cost” in transportation, the “unit” for measuring volume (kilometers, pallets, truckloads), and distinct ways to disaggregate the overall variance into separate components.…”

“…However, there are conceptual differences between manufacturing and transportation, such as the basis for establishing the “standard cost” in transportation, the “unit” for measuring volume (kilometers, pallets, truckloads), and distinct ways to disaggregate the overall variance into separate components. The study by Gaffney et al (2007) demonstrates that the conceptualization of variance analysis of distribution costs depends on specific operational characteristics, such as the presence of a network of plants and warehouses, and the possibility of using alternative modes of transportation (trucks and rail) and alternative carriers. Ernst & Whinney (1983) provides another exception, describing an example of variance analysis of transportation costs, which includes a cost variance analysis due to mix changes (i.e., full truckloads versus less‐than‐full truckloads).…”

Dynamic and Static Decomposition Analysis of the Czech Automotive Production Sector

“…Dynamic analysis of deviations stems from variance analysis. The analysed function on pyramid decomposition is calculated as the variance of a linear function, see e. g. (Dluhošová et al, 2015;Dluhošová et al, 2021;Gaffney, 2007).…”

The application of management accounting techniques to determine the financial viability of delivery routes in the bread industry: a case study

“…However, a Canadian case study was found which proved to be relevant. This case study by Gaffney, Gladkikh and Webb (2007) encompassed the development of a comprehensive variance analysis framework for reporting and controlling distribution costs. Furthermore, Botes, Jacobs and Pienaar ( 2006) developed a model to calculate macro-level logistics costs, using South Africa as a case study.…”